Systematic Pricing and Trading of Municipal Bonds

Abstract

In this article, the authors propose a systematic approach for pricing and trading municipal bonds, leveraging the feature-rich information available at the individual bond level. Based on the proposed pricing framework, they estimate several models using ridge regression and Kalman filtering. In their empirical work, they show that the models compare favorably in pricing accuracy to those available in the literature. Additionally, the models are able to quickly adapt to changing market conditions. Incorporating the pricing models into relative value trading strategies, the authors demonstrate that the resulting portfolios generate significant excess returns and positive alpha relative to the Vanguard Long-Term Tax-Exempt Fund (VWLTX), one of the largest mutual funds in the municipal space.

Keywords: Algorithmic trading, Factor models, Fixed income, Machine learning, Municipal bonds, Pricing models, Relative value, Systematic trading

JEL Classification: C38, C53, C61, E41, G11, G12, H74